将线性光谱分解应用于航空高光谱图像，用于绘制作物产量变异性

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI:10.1109/WHISPERS.2009.5289022

Chenghai Yang, J. Everitt, J. Bradford

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引用次数: 0

摘要

本研究评估了用于绘制作物产量变化的线性光谱分解技术。将无约束线性光谱分解模型和约束线性光谱分解模型分别应用于高粱田和棉田的航空高光谱图像。从每个图像中提取一对植物和土壤光谱作为端元光谱，生成无约束和有约束的植物和土壤覆盖分量。产量与植物组分正相关，与土壤组分负相关。为了进行比较，从102波段图像中计算出所有与产量相关的5151个可能的窄带归一化植被指数(ndvi)。植物组分与产量的相关性优于大多数ndvi。这些结果表明，利用高光谱影像获得的植物覆盖度图可以作为描述作物产量变化的相对产量图。

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Applying linear spectral unmixing to airborne hyperspectral imagery for mapping crop yield variability

This study evaluated linear spectral unmixing techniques for mapping the variation in crop yield. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery recorded from a grain sorghum field and a cotton field. A pair of plant and soil spectra derived from each image was used as endmember spectra to generate unconstrained and constrained plant and soil cover fractions. Yield was positively related to plant fractions and negatively related to soil fractions. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Plant fractions provided better correlations with yield than the majority of the NDVIs. These results indicate that plant cover fraction maps derived from hyperspectral imagery can be used as relative yield maps to characterize crop yield variability.

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来源期刊

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

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